A number of transform-based image coding techniques are known which involve linear transforming a source image to decorrelate data and then encoding the transform coefficients. Such conventional techniques include the JPEG standard image compression method, which employs an 8.times.8 block discrete cosine transform (DCT). JPEG encoding involves transforming blocks of a source image using the DCT, quantising the resultant transform coefficients where most of the compression is effected, and lossless encoding the quantised coefficients in a predefined zig-zag sequence from lowest frequency coefficients to highest frequency coefficients.
There is also a compression technique termed the embedded zerotree wavelet (EZW) method. EZW involves applying a discrete wavelet transform to a source image to decompose the image into a number of high frequency subbands and a lowest frequency subband, normally at a number of different resolution levels or scales. Zero tree encoding is then applied to the subbands dependent upon predictions of the self-similarity of coefficients across scales. The zero-tree-encoded coefficients are then lossless encoded using arithmetic coding.
However, both techniques utilise relatively complex methods for encoding position information and employ lossless encoding. Thus, the foregoing methods have a number of disadvantages including lack of flexibility and complexity in the coding technique.